Uncertainty in Artificial Intelligence
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ARMA Time-Series Modeling with Graphical Models
Bo Thiesson, David Chickering, David Heckerman, Christopher Meek
Abstract:
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it effectively impossible to use the EM algorithm for learning model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA (ARMA) model. This modification allows us to use the EM algorithm to learn parmeters and to forecast,even in situations where some data is missing. This modification, in conjunction with the graphicalmodel approach, also allows us to include cross predictors in situations where there are multiple times series and/or additional nontemporal covariates. More surprising,experiments suggest that the move to stochastic ARMA yields improved accuracy through better smoothing. We demonstrate improvements afforded by cross prediction and better smoothing on real data.
Keywords: null
Pages: 552-560
PS Link:
PDF Link: /papers/04/p552-thiesson.pdf
BibTex:
@INPROCEEDINGS{Thiesson04,
AUTHOR = "Bo Thiesson and David Chickering and David Heckerman and Christopher Meek",
TITLE = "ARMA Time-Series Modeling with Graphical Models",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "552--560"
}


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